Differential privacy has been used to reason about indistinguishability of trajectories of a dynamical system in [1]}, [2]}.
An overview of results that use differential privacy in control is presented in the survey [3]}.
A characterization of differential privacy for discrete-state Markov chains was presented in [4]}, and this was extended to trajectories of discrete MDPs in our previous work [5]}.
Algorithmic guarantees on the differential privacy of policies in RL using Monte-Carlo techniques were provided in [6]}.
An algorithm to synthesize privacy-preserving exploration policies that simultaneously achieved strong regret bounds for episodic RL in discrete environments was proposed in [7]}.
A lower bound for regret minimization in finite horizon MDPs with strong privacy-preserving guarantees was presented in [8]}.
The authors of [9]} developed a policy synthesis algorithm that protected the privacy of transition probabilities of MDPs.
Guarantees on the privacy of value functions of a deep Q-learning algorithm in environments with continuous states using differential privacy were established in [10]}.
Although the above works integrate differential privacy into an MDP or an RL framework, these examine the case where an expected reward needs to be maximized.
In comparison, we establish privacy guarantees on value functions of a CPT-based deep RL algorithm in environments with continuous states.
